AIMC Topic: Algorithms

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Addressing imbalanced data classification with Cluster-Based Reduced Noise SMOTE.

PloS one
In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced N...

One-shot learning for generalization in medical image classification across modalities.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Generalizability is one of the biggest challenges hindering the advancement of medical sensing technologies across multiple imaging modalities. This issue is further impaired when the imaging data is limited in scope or of poor quality. To tackle thi...

Bayesian Optimization-Enhanced Reinforcement learning for Self-adaptive and multi-objective control of wastewater treatment.

Bioresource technology
Controllers of wastewater treatment plants (WWTPs) often struggle to maintain optimal performance due to dynamic influent characteristics and the need to balance multiple operational objectives. In this study, Reinforcement Learning (RL) algorithms a...

Generating synthetic past and future states of Knee Osteoarthritis radiographs using Cycle-Consistent Generative Adversarial Neural Networks.

Computers in biology and medicine
Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limita...

An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis.

Scientific reports
Coronary heart disease (CHD) is the world's leading cause of death, contributing to a high mortality rate. This emphasizes the requirement for an advanced decision support system in order to evaluate the risk of CHD. This study presents an Artificial...

Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.

Scientific reports
Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This pape...

Conditional similarity triplets enable covariate-informed representations of single-cell data.

BMC bioinformatics
BACKGROUND: Single-cell technologies enable comprehensive profiling of diverse immune cell-types through the measurement of multiple genes or proteins per individual cell. In order to translate immune signatures assayed from blood or tissue into powe...

Using deep feature distances for evaluating the perceptual quality of MR image reconstructions.

Magnetic resonance in medicine
PURPOSE: Commonly used MR image quality (IQ) metrics have poor concordance with radiologist-perceived diagnostic IQ. Here, we develop and explore deep feature distances (DFDs)-distances computed in a lower-dimensional feature space encoded by a convo...

Alzheimer's disease classification using hybrid loss Psi-Net segmentation and a new hybrid network model.

Computational biology and chemistry
Alzheimer's disease (AD) is a type of brain disorder that is becoming more prevalent worldwide. It is a progressive and irreversible condition that gradually impairs memory and cognitive abilities, eventually making it difficult to perform even basic...

Augmenting interaction effects in convolutional networks with taylor polynomial gated units.

Neural networks : the official journal of the International Neural Network Society
Transformer-based vision models are often assumed to have an advantage over traditional convolutional neural networks (CNNs) due to their ability to model long-range dependencies and interactions between inputs. However, the remarkable success of pur...